Infectious diseases, including influenza, measles, and sexually transmitted diseases, spread from person to person. Different attempts have been made to modify or extend traditional epidemic models to relax homogeneity assumptions, so as to handle more complex and realistic situations. We propose a network-based approach to the modeling and prediction of infectious disease outbreaks.
Our focus is on heterogeneous populations where there is variation in individual susceptibility, infectivity, and person-to-person contact patterns. To address the complexity of disease propagation over a contact network, we develop a Bayesian survival model that maps the network onto a latent space and uses latent positions to predict disease transmission.
We present an R package (`epinet') implementation of our methods and an application to a high school contact network. The package uses C code to implement an MCMC algorithm to efficiently estimate parameters and predict disease outcomes. Our application involves contact data collected by mobile sensors distributed to individuals, and provides estimates of disease transmission in line with the network structure. In it, we address issues that are of direct interest to public health professionals, such as prediction of future outbreaks of diseases. Questions such as whether quarantine will help mitigate an outbreak can also be explored using our proposed model.